Cumulative culture spontaneously emerges in artificial navigators who are social and memory-guided

• URL: http://arxiv.org/abs/2206.06281v3
• Date: Tue, 25 Jul 2023 07:58:13 GMT
• Title: Cumulative culture spontaneously emerges in artificial navigators who are social and memory-guided
• Authors: Edwin S. Dalmaijer
• Abstract summary: Cumulative cultural evolution occurs when adaptive innovations are passed down to consecutive generations through social learning. This process has shaped human technological innovation, but also occurs in non-human species. I show that a much simpler system suffices. Cumulative culture spontaneously emerged in artificial agents who navigate with a minimal cognitive architecture of goal-direction, social proximity, and route memory.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cumulative cultural evolution occurs when adaptive innovations are passed down to consecutive generations through social learning. This process has shaped human technological innovation, but also occurs in non-human species. While it is traditionally argued that cumulative culture relies on high-fidelity social transmission and advanced cognitive skills, here I show that a much simpler system suffices. Cumulative culture spontaneously emerged in artificial agents who navigate with a minimal cognitive architecture of goal-direction, social proximity, and route memory. Within each generation, naive individuals benefitted from being paired with experienced navigators because they could follow previously established routes. Crucially, experienced navigators also benefitted from the presence of naive individuals through regression to the goal. As experienced agents followed their memorised path, their naive counterparts (unhindered by route memory) were more likely to err towards than away from the goal, and thus biased the pair in that direction. This improved route efficiency within each generation. In control experiments, cumulative culture was attenuated when agents' social proximity or route memory were lesioned, whereas eliminating goal-direction only reduced efficiency. These results demonstrate that cumulative cultural evolution occurs even in the absence of sophisticated communication or thought. One interpretation of this finding is that current definitions are too loose, and should be narrowed. An alternative conclusion is that rudimentary cumulative culture is an emergent property of systems that seek social proximity and have an imprecise memory capacity, providing a flexible complement to traditional evolutionary mechanisms.

Related papers

• Can Language Models Learn to Skip Steps? [59.84848399905409]
  We study the ability to skip steps in reasoning. Unlike humans, who may skip steps to enhance efficiency or to reduce cognitive load, models do not possess such motivations. Our work presents the first exploration into human-like step-skipping ability.
  arXiv  Detail & Related papers  (2024-11-04T07:10:24Z)
• Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning [5.930456214333413]
  We show that training setups which balance social learning with independent learning give rise to cultural accumulation. In-context and in-weights cultural accumulation can be interpreted as analogous to knowledge and skill accumulation, respectively. This work is the first to present general models that achieve emergent cultural accumulation in reinforcement learning.
  arXiv  Detail & Related papers  (2024-06-01T10:33:32Z)
• Cultural evolution in populations of Large Language Models [15.012901178522874]
  We propose that leveraging the capacity of Large Language Models to mimic human behavior may be fruitful to address this gap. As artificial agents are bound to participate more and more to the evolution of culture, it is crucial to better understand the dynamics of machine-generated cultural evolution. We present a framework for simulating cultural evolution in populations of LLMs, allowing the manipulation of variables known to be important in cultural evolution.
  arXiv  Detail & Related papers  (2024-03-13T18:11:17Z)
• Generative Adversarial Neuroevolution for Control Behaviour Imitation [3.04585143845864]
  We propose to explore whether deep neuroevolution can be used for behaviour imitation on popular simulation environments. We introduce a simple co-evolutionary adversarial generation framework, and evaluate its capabilities by evolving standard deep recurrent networks. Across all tasks, we find the final elite actor agents capable of achieving scores as high as those obtained by the pre-trained agents.
  arXiv  Detail & Related papers  (2023-04-03T16:33:22Z)
• Memory-Augmented Theory of Mind Network [59.9781556714202]
  Social reasoning requires the capacity of theory of mind (ToM) to contextualise and attribute mental states to others. Recent machine learning approaches to ToM have demonstrated that we can train the observer to read the past and present behaviours of other agents. We tackle the challenges by equipping the observer with novel neural memory mechanisms to encode, and hierarchical attention to selectively retrieve information about others. This results in ToMMY, a theory of mind model that learns to reason while making little assumptions about the underlying mental processes.
  arXiv  Detail & Related papers  (2023-01-17T14:48:58Z)
• Flexible social inference facilitates targeted social learning when rewards are not observable [58.762004496858836]
  Groups coordinate more effectively when individuals are able to learn from others' successes. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behavior.
  arXiv  Detail & Related papers  (2022-12-01T21:04:03Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• Learning Robust Real-Time Cultural Transmission without Human Data [82.05222093231566]
  We provide a method for generating zero-shot, high recall cultural transmission in artificially intelligent agents. Our agents succeed at real-time cultural transmission from humans in novel contexts without using any pre-collected human data. This paves the way for cultural evolution as an algorithm for developing artificial general intelligence.
  arXiv  Detail & Related papers  (2022-03-01T19:32:27Z)
• The Introspective Agent: Interdependence of Strategy, Physiology, and Sensing for Embodied Agents [51.94554095091305]
  We argue for an introspective agent, which considers its own abilities in the context of its environment. Just as in nature, we hope to reframe strategy as one tool, among many, to succeed in an environment.
  arXiv  Detail & Related papers  (2022-01-02T20:14:01Z)
• Investigation into Open-Ended Fitness Landscape through Evolutionary Logical Circuits [11.136938223906984]
  We modify a simulation by Arthur and Polak (2006) that modeled open-ended fitness landscape. We investigate whether the speed of accumulation of culture is increased by an increase in group size.
  arXiv  Detail & Related papers  (2020-02-03T08:21:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.